Titlebook: Genetic Programming; 21st European Confer Mauro Castelli,Lukas Sekanina,Pablo García-Sánchez Conference proceedings 2018 Springer Internati

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https://doi.org/10.1007/978-3-319-03762-2y used in machine learning due to their features: they average out biases, they reduce the variance and they usually generalize better than single models. Despite these advantages, building ensemble of GP models is not a well-developed topic in the evolutionary computation community. To fill this ga

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Designing Quantitative Experiments,gate the feasibility of synthesizing a representation ., for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate representations expressed with an ad hoc language that

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